Optimization of Electric Vehicle Charging Infrastructure: A Strategic Approach Using Clustering and Multi-Criteria Decision-Making Techniques

The strategic placement of Electric Vehicle Charging Stations (EVCSs) plays a vital role in developing the electric vehicle (EV) industry by ensuring accessibility and efficiency. However, selecting optimal EVCS locations is a complex, uncertainty-embedded multi-criteria decision-making (MCDM) problem involving both quantitative and qualitative factors. This study proposes a comprehensive methodology to enhance EVCS distribution by minimizing the average distance between charging stations, increasing EVCS density, and improving their integration with public facilities. The research focuses on central Chennai and its surrounding suburban areas. The methodology involves identifying existing EVCS locations, analyzing their spatial distribution, and detecting gaps in coverage. Cluster Analysis is applied to group proposed EVCS locations based on spatial proximity, with the optimal number of clusters determined using the Silhouette Score and Davies Bouldin Index. Selection criteria for EVCS placement are established using expert opinions and data collection, and their relative importance is computed using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method. The Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) is then used to select the most suitable EVCS locations within each cluster. The findings reveal an 11.12% reduction in the average distance between charging stations, a 55.56% increase in EVCS density, and a 22% improvement in the integration of EVCS with public facilities. This integrated approach ensures a balanced and well-distributed EVCS network, effectively addressing the current infrastructure challenges in the study area.